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Fr\'echet Inception Distance (FID) is the primary metric for ranking models in data-driven generative modeling.
The Fréchet distance between multivariate normal distributions
D. Dowson and B. Landau · 1982
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Kernel Measures of Conditional Dependence
Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, and Bernhard Schölkopf · 2007
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Formulas for robust, one-pass parallel computation of covariances and arbitrary-order statistical moments
Philippe Pierre Pebay · 2008
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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A Kernel Two-Sample Test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Generative Adversarial Networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2014
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and S. Ganguli · 2015
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LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
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Deep Residual Learning for Image Recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
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Improved Techniques for Training GANs
Tim Salimans, I. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Rethinking the Inception Architecture for Computer Vision
Christian Szegedy, V. Vanhoucke, S. Ioffe, Jonathon Shlens, and Z. Wojna · 2016
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Density Estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew P. Aitken, Alykhan Tejani, J. Totz, Zehan Wang, and W. Shi · 2017
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2017
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Demystifying MMD GANs
Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, and A. Gretton · 2018
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Glow: Generative Flow with Invertible 1x1 Convolutions
Diederik P. Kingma and Prafulla Dhariwal · 2018
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Are GANs Created Equal? A Large-Scale Study
Mario Lucic, Karol Kurach, Marcin Michalski, S. Gelly, and O. Bousquet · 2018
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Assessing Generative Models via Precision and Recall
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
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An Empirical Study on Evaluation Metrics of Generative Adversarial Networks
Qiantong Xu, Gao Huang, Yang Yuan, Chuan Guo, Yu Sun, Felix Wu, and Kilian Q. Weinberger · 2018
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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Blind Super-Resolution Kernel Estimation using an Internal-GAN
Sefi Bell-Kligler, Assaf Shocher, and Michal Irani · 2019
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Large Scale GAN Training for High Fidelity Natural Image Synthesis
Andrew Brock, Jeff Donahue, and K. Simonyan · 2019
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix Wichmann, and Wieland Brendel · 2019
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A Style-Based Generator Architecture for Generative Adversarial Networks
Tero Karras, S. Laine, and Timo Aila · 2019
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Improved Precision and Recall Metric for Assessing Generative Models
Taming Transformers for High-Resolution Image Synthesis
Patrick Esser, Robin Rombach, and Björn Ommer · 2021
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Alias-Free Generative Adversarial Networks
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2021
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Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Bingchen Liu, Yizhe Zhu, Kunpeng Song, and Ahmed Elgammal · 2021
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On Self-Supervised Image Representations for GAN Evaluation
Stanislav Morozov, Andrey Voynov, and Artem Babenko · 2021
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Generating Images with Sparse Representations
Charlie Nash, Jacob Menick, S. Dieleman, and P. Battaglia · 2021
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Learning Transferable Visual Models From Natural Language Supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
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Semantic Image Synthesis With Spatially-Adaptive Normalization
Taesung Park, Ming-Yu Liu, T. Wang, and Jun-Yan Zhu · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Generating Diverse High-Fidelity Images with VQ-VAE-2
Ali Razavi, Aäron van den Oord, and Oriol Vinyals · 2019
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Generative Modeling by Estimating Gradients of the Data Distribution
Yang Song and Stefano Ermon · 2019
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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan and Quoc V. Le · 2019
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Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Zero-Shot Text-to-Image Generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Image super-resolution via iterative refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi · 2021
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Projected GANs Converge Faster
Axel Sauer, Kashyap Chitta, Jens Müller, and Andreas Geiger · 2021
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On the Robustness of Quality Measures for GANs
Motasem Alfarra, Juan C. Pérez, Anna Frühstück, Philip H. S. Torr, Peter Wonka, and Bernard Ghanem · 2022
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Pros and cons of GAN evaluation measures: New developments
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Multimodal Conditional Image Synthesis with Product-of-Experts GANs
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MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining
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Latent reweighting, an almost free improvement for GANs
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Ensembling Off-the-shelf Models for GAN Training
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
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On Aliased Resizing and Surprising Subtleties in GAN Evaluation
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Hierarchical Text-Conditional Image Generation with CLIP Latents
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Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
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StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets
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Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality
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